Recap: central dogma & omics

Alternative splicing & isoforms

https://en.wikipedia.org/wiki/Protein_isoform

The transcriptome

The transcriptome is the full set of transcripts expressed by an organism, which:

Transcriptomics

Transcriptomics is the study of the transcriptome, i.e. the large-scale study of RNA transcripts expressed in an organism.

There are many approaches & applications — but most commonly, transcriptomics focuses on:

Transcriptomics

Transcriptomics is the study of the transcriptome, i.e. the large-scale study of RNA transcripts expressed in an organism.

There are many approaches & applications — but most commonly, transcriptomics focuses on:

• mRNA rather than on noncoding RNA types such as rRNA, tRNA, and miRNA

• Quantifying gene expression levels (cf. most genomics approaches)

• Statistically comparing expression levels between groups (treatments, biotypes, tissues, etc.)

Why do transcriptomics?

Considering…

• That protein production gives clues about the activity of specific biological functions, and the molecular mechanisms underlying those functions;

• That it is much easier to measure protein expression than transcript expression at large scales;

• The central dogma

Why do transcriptomics? (cont.)

Specifically, we can use transcriptomics to:

• Compare & contrast phenotypic vs. molecular responses/differences

What is RNA-seq?

RNA-seq is the current state-of-the-art family of methods to study the transcriptome.

It involves the random (“shotgun”) sequencing of millions of transcript fragments per sample.

We will focus on the most common type of RNA-seq, which:

Other RNA-seq applications

RNA-seq data can also be used for applications other than expression quantification, such as to:

• Identify & analyze SNPs (for population genetics, molecular evolution, functional associations, etc)

RNA-seq project examples

RNA-seq is also the most common data type I assist with as a bioinformatician at the MCIC. Some projects I’ve been involved in used RNA-seq to identify genes & pathways that differ between:

• Multiple soybean cultivars in response to Phytophtora sojae inoculation; soybean in response to different Phytophtora species and strains (Dorrance lab, PlantPath)

• Wheat exposed to Xanthomonas with a gene knock-out vs. knock-in (Jacobs lab, PlantPath)

RNA-seq project examples (cont.)

[TODO: 1 or 2 figs, e.g. from Tracy]

Other ways of quantifying gene expression

RNA-seq is not the only method to quantify gene expression, but often the preferred one:

• Reverse-transcription qPCR (RT-qPCR)
  A low-throughput method that can quantify expression levels for one or a few genes at a time

Some other types of RNA-seq

• Small RNA-seq for small noncoding RNAs such as microRNAs and Piwi-interacting RNAs

• Single-cell RNA-seq (scRNA-seq) — commonly used in cancer research

• Direct RNA sequencing

Experimental design: groups & replicates

RNA-seq typically compares groups of samples defined by differences in:

• Treatments (e.g. different host plant, temperature, diet, mated/unmated) and/or

• Organismal variants: ages/developmental stages, sexes, or genotypes (lines/biotypes/subspecies/morphs) and/or

• Tissues

Experimental design: groups & replicates

https://github.com/ScienceParkStudyGroup/rnaseq-lesson

Experimental design: groups & replicates

To be able to make statistically supported conclusions about expression differences between such groups of samples, we must have biological replication.

When designing an RNA-seq experiment, keep the following in mind:

• Numbers of replicates
  These are typically quite low: 3 replicates per treatment (x tissue x biotype, etc.) is the most common. Not advisable to go lower — if possible, use 4 or 5 replicates.

Experimental design side note

From samples to reads: overview of steps

https://sydney-informatics-hub.github.io/training-RNAseq-slides

Overview of library prep steps

RNA extraction

The first step is to extract RNA from tissue samples. This is typically the only step you conduct yourself in the lab, with sequencing facilities taking care of downstream procedures.

Some considerations and recommendations:

RIN (RNA Integrity Number) score

Library prep options

As discussed last week, “library preparation” is the series of lab steps to produce, from (DNA or) RNA extractions, a collection of molecules ready to be sequenced.

Let’s consider the different options you have for RNA-seq library prep:

Sequencing

• Sequencing technology

  • Illumina short reads are by far the most common

  • PacBio or ONT long reads are an alternative if sequencing full transcripts (isoforms) is key

Number of replicates vs. sequencing depth

For statistical power, more replicates are better than a higher sequencing depth:

From Liu et al. 2014

Overview of steps

Modified after Kukurba & Montgomery 2015

From reads to counts: overview

You will typically receive a “demultiplexed” (split by sample) set of FASTQ files.

Once you receive your data, the first series of analysis steps involves going from the raw reads to a count table (the count table will have a read count for each gene in each sample).

Reads to counts: Read pre-processing

Read pre-processing includes the following steps:

• Checking the quantity and quality of your reads

  • Does not change your data, but helps decide next pre-processing steps / sample exclusion

  • Also useful to check for contamination, library complexity, and adapter content

  • Typically done with the tool FastQC (and MultiQC for across-sample summaries)

Recap: isoforms & alternative splicing

Reads to counts: Alignment to a reference genome

The alignment of reads to a reference genome needs to be “splice-aware”.
Alternatively, you can align to the transcriptome (i.e., all mature transcripts).

Berge et al. 2019 (www.annualreviews.org/doi/10.1146/annurev-biodatasci-072018-021255)

Reads to counts: Alignment to a reference genome

The alignment of reads to a reference genome needs to be “splice-aware”.
Alternatively, you can align to the transcriptome (i.e., all mature transcripts).

Alignment usually consists of two steps:

• One-time creation of a tool-specific “index” of the reference genome/transcriptome.

• For each sample independently, alignment of the reads to the reference index.

What if I don’t have a reference genome?

Without a reference genome, an RNA-seq project is considerably more work.
These two extra steps are needed:

• De novo transcriptome assembly
  First, your reads can be used to first assemble a transcriptome from scratch with a tool like Trinity. (This is quite compute-intensive!)

Reads to counts: alignment QC

These kind of stats can only be assessed after aligning to the genome (vs. to the transcriptome)¹.

• Alignment rates
  What percentage of reads was successfully aligned?

  • Low rates (<80%) => cross-species contamination or poor reference genome quality

  • Multi-mapped reads (mapping to multiple locations in the genome) are typically accepted by the mapper but are not or probabilistically quantified — more on this later.

Reads to counts: alignment QC

• Across-gene coverage distribution
  An uneven distribution may indicate high levels of RNA degradation

• Outlier samples
  Are patterns consistent across samples or are there outliers that may need to be removed?

Reads to counts: quantification

At heart, a simple counting exercise once you have the alignments in hand.
But made more complicated by sequencing biases and multi-mapping reads.

A best-practice workflow to produce counts

The “nf-core” initiative (https://nf-co.re) attempts to produce best-practice workflows/pipelines using the Nextflow workflow language.

This entire workflow can be run with a single command!

https://nf-co.re/rnaseq

Count table analysis: overview

The second part of RNA-seq data analysis involves analyzing the count table.
In contrast to the first part, this can be done on a laptop and instead is heavier on statistics, data visualization and biological interpretation.

PCA &

Two or more groups are typically compared, such as plants exposed to virulent vs. avirulent aphids.

• A PCA can tell you whether there’s an overall difference in the plant’s transcriptional response between treatments

Fig. 1 from Garrigos et al. 2023

Differential expression (DE) analysis

• A Differential Expression (DE) analysis allows you to quantify the magnitude of this difference, and especially to pinpoint specific genes with statistically significant differences in expression levels (Differentially Expressed Genes, DEGs).

[TODO: Volcano plot and/or gene plot]

DE analysis: >1 pairwise comparison

More than two groups

DE analysis: other designs

Statistical designs other than pairwise comparisons are also possible. For example:

• Time series (or other continuous variables; regression analysis)
  Which genes have monotonous in- or decreases across three or more time points?

DE analysis: general statistical considerations

• Gene count normalization
  To be able to fairly compare samples, raw counts need to be adjusted:

  • By library size, which is the total number of gene counts per sample
  • By library composition, e.g. to correct for sample-specific extremely abundant genes that “steal” most of that sample’s counts

DE analysis: general statistical considerations (cont.)

• Multiple-testing correction

  • 10,000+ genes are independently tested during a DE analysis, so there is a dire need for multiple testing correction.

  • The standard method is not the very strict Bonferroni correction but the Benjamini-Hochberg (BH) method.

Functional enrichment: introduction

Lists of DEGs can be quite long, and it is not always easy to make biological sense of them. Functional enrichment analyses help with this.

They check whether certain functional categories of genes (e.g., biological processes, or pathways) are statistically overrepresented among up- and/or downregulated genes.

Functional enrichment: database overview

There are a number of databases that group genes into functional categories, but the two main ones used for enrichment analysis are:

• Gene Ontology (GO)

• Kyoto Encyclopedia of Genes and Genomes (KEGG)

Functional enrichment: GO

• Genes are assigned zero, one or more GO “terms”

• Hierarchical structure with more specific terms grouping into more general terms

• Consists of three “ontologies”:

  • Biological Process

  • Molecular Function

  • Cellular Component

Functional enrichment: KEGG

• Orthologs, modules and pathways

• Contains fewer genes than GO

[Include pathway figure]

Functional enrichment: statistics

The two most common statistical approaches are:

• Overrepresentation Analysis (ORA)
  With a list of genes divided in two groups (DE vs. not-DE¹), test whether the groups contain different frequencies of each functional gene category, like a Chi-Square/Fisher’s Exact Test: